INTRODUCTION







Design Contest - Tennis GrandSlam Data


Dated: 10/27/2019










-Presented by
Roma Dutta
Ganesh Viswanathan
GitHub-https://github.com/ganesh2512/DSBA/tree/master/designContest_Ganesh_Roma
TidyTuesday#-spren9er RStudioLink-https://rstudio.cloud/project/642289


RESEARCH

Grand Slam results from 1968-2019 of some of the best female and male tennis players in modern era.


CRITICAL REVIEW

Overall The graph looks awesome -But

The graph provides lot of data within a single view. Its has details on winners with names along with the type of tourney and year. And is well presented.

1-Color Repeated

The color used for grandslam types and place in tourney are repeated and hence confusing the view

2-Color blind Issue

Color blind scheme could be used to make it easier for people with disabilities.

3-Comparison by year across players not possible

Comparison of how a player progressed on grandslam with his age compared to others is not possible to figure out

4-Arrangement of folks

Arrange of folks looks a little complex. Could have been better arranged as a bar with multiple axes.

5-Year could be in ascending order

The year comparison of when the player won more grandslams is not possible as arrangement is in chronological order


IMPROVISATION

  • Different color options have been used for “Tournament” and “Tournament Outcome” to better comprehend

  • We have applied Bang wong and Zesty color palettes that takes care of color blidness

  • We have arranged the players by total number of wins






















PROPOSED DESIGN


NEW IDEAS

---
title: "**DesignContest**"
output:
  flexdashboard::flex_dashboard:
    logo: 'images/rsz_grandslam.png'
    theme: spacelab
    storyboard: true
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
```
# INTRODUCTION






Design Contest - Tennis GrandSlam Data


Dated: 10/27/2019










-Presented by
Roma Dutta
Ganesh Viswanathan
GitHub-https://github.com/ganesh2512/DSBA/tree/master/designContest_Ganesh_Roma
TidyTuesday#-spren9er RStudioLink-https://rstudio.cloud/project/642289

```{r out.width = "100%", out.height = "100%"} ``` *** # RESEARCH ### Grand Slam results from 1968-2019 of some of the best female and male tennis players in modern era.
```{r out.width = "100%", out.height = "60%"} knitr::include_graphics('images/tidytuesday_201915_tennis_grand_slams.gif') ```
- Data source -Tidy Tuesday https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-04-09


- Code Source: Dr. Torsten Sprenger's tidytusday post on Apr 9 https://github.com/spren9er/tidytuesday https://github.com/spren9er/tidytuesday/blob/master/tidytuesday_201915_tennis_grand_slams.r























*** # CRITICAL REVIEW ### Overall The graph looks awesome -But #### The graph provides lot of data within a single view. Its has details on winners with names along with the type of tourney and year. And is well presented. ### 1-Color Repeated #### The color used for grandslam types and place in tourney are repeated and hence confusing the view ### 2-Color blind Issue #### Color blind scheme could be used to make it easier for people with disabilities. ### 3-Comparison by year across players not possible #### Comparison of how a player progressed on grandslam with his age compared to others is not possible to figure out ### 4-Arrangement of folks #### Arrange of folks looks a little complex. Could have been better arranged as a bar with multiple axes. ### 5-Year could be in ascending order #### The year comparison of when the player won more grandslams is not possible as arrangement is in chronological order ```{r out.width = "100%", out.height = "100%"} ``` *** # IMPROVISATION ####
```{r out.width = "100%", out.height = "60%"} knitr::include_graphics('images/tidytuesday_201915_tennis_grand_slams_modified.gif') ```
- Different color options have been used for "Tournament" and "Tournament Outcome" to better comprehend

- We have applied Bang wong and Zesty color palettes that takes care of color blidness

- We have arranged the players by total number of wins





















*** # PROPOSED DESIGN
```{r out.width = "100%", out.height = "90%"} knitr::include_graphics('images/Capture_final.JPG') ```
*** # NEW IDEAS ## ```{r out.width = "50%", out.height = "50%", warning=FALSE, echo=FALSE} library(tidyverse) library(scales) # Import data player_dob <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/player_dob.csv") grand_slams <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slams.csv") grand_slam_timeline <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slam_timeline.csv") # Add a case statement for court types grand_slams <- grand_slams %>% mutate(court_type = case_when(grand_slam == "australian_open" ~ "Hard Court", grand_slam == "us_open" ~ "Hard Court", grand_slam == "french_open" ~ "Clay Court", grand_slam == "wimbledon" ~ "Grass Court")) # Set theme for charts #theme_set(theme_classic()) # Plot Top Ten Winners grand_slams %>% count(name, court_type, sort = TRUE) %>% add_count(name, wt = n) %>% filter(n >=5) %>% mutate(name = fct_reorder(name, n, sum)) %>% ggplot(aes(name, n, fill = court_type)) + geom_col() + scale_fill_manual(values = c('#F6BD60', '#7FB069', '#548687')) + coord_flip() + labs(x = "", y = "No. of Grand Slam Wins", title = "Top Grand Slam Winners By Court Type", subtitle = "1968 - 2019") + theme(legend.position="top", legend.title = element_blank(), legend.spacing.x = unit(0.2, 'cm'), plot.title=element_text(size=12,face="bold"), plot.subtitle=element_text(face="italic",size=11,colour="grey40")) aspect_ratio <- 2 ggsave("top_players_court.png", height = 5 , width = 5 * aspect_ratio) ``` ## ```{r out.width = "50%", out.height = "50%",} library(tidyverse) library(scales) # Import data player_dob <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/player_dob.csv") grand_slams <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slams.csv") grand_slam_timeline <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slam_timeline.csv") # Calculate age # Add a case statement for court types grand_slams <- grand_slams %>% mutate(court_type = case_when(grand_slam == "australian_open" ~ "Hard Court", grand_slam == "us_open" ~ "Hard Court", grand_slam == "french_open" ~ "Clay Court", grand_slam == "wimbledon" ~ "Grass Court")) age <- player_dob %>% select(name, date_of_birth) %>% inner_join(grand_slams, by = "name") %>% mutate(age = as.numeric(difftime(tournament_date, date_of_birth, unit = "days"))/365) # Boxplot of player ages by tournament age %>% mutate(grand_slam = str_to_title(str_replace(grand_slam, "_", " "))) %>% ggplot(aes(grand_slam, age, fill = gender)) + geom_boxplot() + scale_fill_manual(values = c('#8700F9', '#00C4AA')) + labs(x = "Grand Slam", y = "Age", title = "Distribution of Age By Grand Slam") + theme(legend.position="top", legend.title = element_blank(), legend.spacing.x = unit(0.2, 'cm'), plot.title=element_text(size=12,face="bold")) aspect_ratio <- 2 ggsave("images/grand_slams_age_distribution.png", height = 5 , width = 5 * aspect_ratio) ```
Courtesy: https://jaredbraggins.com/2019/04/grand-slam-winners/